Here Comes Trouble: Prestimulus Brain Activity Predicts Enhanced Perception of Threat
Bibliographic record
Abstract
Research on the perceptual prioritization of threatening stimuli has focused primarily on the physical characteristics and evolutionary salience of these stimuli. However, perceptual decision-making is strongly influenced by prestimulus factors such as goals, expectations, and prior knowledge. Using both event-related potentials and functional magnetic resonance imaging, we test the hypothesis that prior threat-related information and related increases in prestimulus brain activity play a key role in subsequent threat-related perceptual decision-making. After viewing threatening and neutral cues, participants detected perceptually degraded threatening and neutral faces presented at individually predetermined perceptual thresholds in a perceptual decision-making task. Compared with neutral cues, threat cues resulted in (1) improved perceptual sensitivity and faster detection of target stimuli; (2) increased late positive potential (LPP) and superior temporal sulcus (STS) activity, both of which are measures of emotional face processing; and (3) increased amygdala activity for subsequently presented threatening versus and neutral faces. Importantly, threat cue-related LPP and STS activity predicted subsequent improvement in the speed and precision of perceptual decisions specifically for threatening faces. Present findings establish the importance of top-down factors and prestimulus neural processing in understanding how the perceptual system prioritizes threatening information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".